demonstration

Demonstrating OmniCells: a resilient indoor localization system to devices' diversity

Authors:

Hamada Rizk,

Tatsuya Amano,

Hirozumi Yamaguchi,

Moustafa YoussefAuthors Info & Claims

MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

Pages 781 - 782

https://doi.org/10.1145/3495243.3558753

Published: 14 October 2022 Publication History

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Abstract

In this paper, we demonstrate OmniCells: a cellular-based indoor localization system designed to combat the device heterogeneity problem. OmniCells is a deep learning-based system that leverages cellular measurements from one or more training devices to provide consistent performance across unseen tracking phones. In this demo, we show the effect of device heterogeneity on the received cellular signals and how this leads to performance deterioration of traditional localization systems. In particular, we show how OmniCells and its novel feature extraction methods enable learning a rich and device-invariant representation without making any assumptions about the source or target devices. The system also includes other modules to increase the deep model's generalization and resilience to unseen scenarios.

References

[1]

Hamada Rizk. 2019. Device-Invariant Cellular-Based Indoor Localization System Using Deep Learning. In MobiSys (RisingStarsForum'19). ACM, 19--23.

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[2]

Hamada Rizk. 2019. Solocell: Efficient indoor localization based on limited cell network information and minimal fingerprinting. In ACM SIGSPATIAL. 604--605.

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[3]

Hamada Rizk, Moustafa Abbas, and Moustafa Youssef. 2020. OmniCells: Cross-Device Cellular-based Indoor Location Tracking Using Deep Neural Networks. In 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom). 1--10.

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[4]

Hamada Rizk, Ahmed Elmogy, and Hirozumi Yamaguchi. 2022. A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI. Sensors 22, 7 (2022).

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[5]

Hamada Rizk, Dong Ma, Mahbub Hassan, and Moustafa Youssef. 2022. Indoor Localization using Solar Cells. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). 38--41.

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[6]

Hamada Rizk, Marwan Torki, and Moustafa Youssef. 2018. CellinDeep: Robust and Accurate Cellular-based Indoor Localization via Deep Learning. IEEE Sensors Journal (2018).

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[7]

Hamada Rizk, Hirozumi Yamaguchi, Moustafa Youssef, and Teruo Higashino. 2022. Laser Range Scanners for Enabling Zero-Overhead WiFi-Based Indoor Localization System. ACM Trans. Spatial Algorithms Syst. (2022).

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[8]

Hamada Rizk and Moustafa Youssef. 2019. MonoDCell: A Ubiquitous and Low-Overhead Deep Learning-based Indoor Localization with Limited Cellular Information. In Proceedings of the 27th ACM SIGSPATIAL. ACM, 109--118.

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Shintani KRizk HYamaguchi H(2023)Photovoltaic Cells for Energy Harvesting and Step Counting2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150258(316-318)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PerComWorkshops56833.2023.10150258
Gufran DTiku SPasricha S(2023)STELLAR: Siamese Multiheaded Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity With Indoor LocalizationIEEE Journal of Indoor and Seamless Positioning and Navigation10.1109/JISPIN.2023.33346931(115-129)Online publication date: 2023
https://doi.org/10.1109/JISPIN.2023.3334693

Index Terms

Demonstrating OmniCells: a resilient indoor localization system to devices' diversity
1. Human-centered computing
  1. Ubiquitous and mobile computing

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MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

October 2022

932 pages

ISBN:9781450391818

DOI:10.1145/3495243

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 October 2022

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JSPS, KAKENHI, Japan

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ACM MobiCom '22

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SIGMOBILE

ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking

October 17 - 21, 2022

NSW, Sydney, Australia

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Shintani KRizk HYamaguchi H(2023)Photovoltaic Cells for Energy Harvesting and Step Counting2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150258(316-318)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PerComWorkshops56833.2023.10150258
Gufran DTiku SPasricha S(2023)STELLAR: Siamese Multiheaded Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity With Indoor LocalizationIEEE Journal of Indoor and Seamless Positioning and Navigation10.1109/JISPIN.2023.33346931(115-129)Online publication date: 2023
https://doi.org/10.1109/JISPIN.2023.3334693

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Device-Invariant Cellular-Based Indoor Localization System Using Deep Learning

Device-independent cellular-based indoor location tracking using deep learning

MonoDCell: A Ubiquitous and Low-Overhead Deep Learning-based Indoor Localization with Limited Cellular Information

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Device-Invariant Cellular-Based Indoor Localization System Using Deep Learning

Device-independent cellular-based indoor location tracking using deep learning

MonoDCell: A Ubiquitous and Low-Overhead Deep Learning-based Indoor Localization with Limited Cellular Information

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